The identity of metal ions surrounding DNA is key to its biological function and materials applications. In this work, we compare atomistic molecular dynamics simulations of double strand DNA (dsDNA) with four alkaline earth metal ions (Mg2+, Ca2+, Sr2+, and Ba2+) to elucidate the physical interactions that govern DNA–ion binding. Simulations accurately model the ion–phosphate distance of Mg2+ and reproduce ion counting experiments for Ca2+, Sr2+, and Ba2+. Our analysis shows that alkaline earth metal ions prefer to bind at the phosphate backbone compared to the major groove and negligible binding occurs in the minor groove. Larger alkaline earth metal ions with variable first solvation shells (Ca2+, Sr2+, and Ba2+) show both direct and indirect binding, where indirect binding increases with ion size. Mg2+ does not fit this trend because the strength of its first solvation shell predicts indirect binding only. Ions bound to the phosphate backbone form fewer contacts per ion compared to the major groove. Within the major groove, metal ions preferentially bind to guanine–cystosine base pairs and form simultaneous contacts with the N7 and O6 atoms of guanine. Overall, we find that the interplay among ion size, DNA–ion interaction, and the size and flexibility of the first solvation shell are key to predicting how alkaline earth metal ions interact with DNA.

We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density evolution, producing highly accurate results even when propagating 1,000 time steps beyond the training data. As a more rigorous test, we use the learned Hamiltonians to simulate electron dynamics in the presence of an applied electric field, extrapolating to a problem that is beyond the field-free training data. We find that the resulting electron dynamics predicted by our learned Hamiltonian are in close quantitative agreement with the ground truth. Our method relies on combining a reduced-dimensional, linear statistical model of the Hamiltonian with a time-discretization of the quantum Liouville equation within time-dependent Hartree Fock theory. We train the model using a least-squares solver, avoiding numerous, CPU-intensive optimization steps. For both field-free and field-on problems, we quantify training and propagation errors, highlighting areas for future development.

The presence of solvent tunes many properties of a molecule, such as its ground and excited state geometry, dipole moment, excitation energy, and absorption spectrum. Because the energy of the system will vary depending on the solvent configuration, explicit solute–solvent interactions are key to understanding solution-phase reactivity and spectroscopy, simulating accurate inhomogeneous broadening, and predicting absorption spectra. In this tutorial review, we give an overview of factors to consider when modeling excited states of molecules interacting with explicit solvent. We provide practical guidelines for sampling solute–solvent configurations, choosing a solvent model, performing the excited state electronic structure calculations, and computing spectral lineshapes. We also present our recent results combining the vertical excitation energies computed from an ensemble of solute–solvent configurations with the vibronic spectra obtained from a small number of frozen solvent configurations, resulting in improved simulation of absorption spectra for molecules in solution.